CN109120011B - distributed power distribution network congestion scheduling method considering distributed power sources - Google Patents
distributed power distribution network congestion scheduling method considering distributed power sources Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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Abstract
the invention provides a distributed power distribution network congestion scheduling method considering distributed power sources, and belongs to the technical field of operation and control of power systems. Firstly, establishing a distributed power distribution network congestion scheduling optimization model which is composed of an objective function and constraint conditions and takes distributed power sources into consideration; relaxing the model; and after the model is converted by defining new variables, carrying out distributed iterative solution on the model to obtain a distributed power distribution network congestion scheduling scheme considering the distributed power supply. The method of the invention considers the influence and the effect brought by the distributed power supply, gives full play to the flexible advantage of the distributed power supply, reduces the congestion degree of the network, and adopts the distributed method to solve and calculate the model, thereby being capable of quickly reducing the local congestion degree.
Description
Technical Field
the invention belongs to the technical field of operation and control of power systems, and particularly relates to a distributed power distribution network congestion scheduling method considering distributed power sources.
Background
the traditional power system is formed by longitudinally connecting power generation, transmission and distribution networks, and the main constituent elements of the traditional power system are a large unit and a large power grid. In recent years, with increasingly prominent environmental problems, traditional power systems have been changed greatly in terms of social economy and technological development. The wide access of the distributed renewable energy sources to the distribution network side brings new challenges and opportunities to the development of the traditional power system. If the power distribution network is not reasonably regulated, a plurality of power supplies and loads compete for using limited power transmission and distribution lines, and the power value of each branch in the power distribution network approaches to the power value of the branch, so that the safe operation of the power distribution network is threatened. How to avoid the congestion phenomenon in the power distribution network by reasonably scheduling network resources is an important subject.
The key point of the congestion scheduling of the multi-main-body active power distribution network is that how to minimize the congestion degree of the power distribution network by means of cooperative mutual assistance among regions on the premise of meeting the security constraints (including node voltage and branch flow) of a global system. To achieve this goal, the distributed optimal power flow of the active power distribution network will be one of the most powerful means, and this can be divided into applications of both reactive and active optimal scheduling. The access of the large-scale distributed power supply enables the problem that the voltage of the active power distribution network is out of limit easily to occur during operation through the action of reverse power flow, and at the moment, optimal scheduling of reactive equipment in the power distribution network enables network loss to be minimized, and meanwhile, the operation safety constraint of a global system is guaranteed, which is particularly necessary; on the other hand, with the development of power electronic technology, the controllability of the distributed power supply is also enhanced, reactive load is generally difficult to predict in engineering practice, the active output of the distributed power supply and the energy storage in each region is optimally scheduled, the global congestion degree of the power distribution network is reduced, and further research is worth.
However, optimal power flow is mathematically a non-convex, NP-hard problem. How to solve the centralized problem efficiently is still one of the most challenging problems of the power system, and still receives a lot of attention and research. Meanwhile, compared with a centralized algorithm, the distributed optimization has more severe calculation conditions, each region only has local information, and the communication conditions between the regions are generally limited to a certain extent, so that the distributed distribution network congestion scheduling method considering the distributed power supply faces more severe challenges.
In the early distributed power distribution network congestion scheduling method, on one hand, a non-convex problem is usually solved by directly using a distributed algorithm, and under the general condition, the distributed optimal power flow algorithm is not proved by strict convergence. Especially, for a distributed algorithm based on dual decomposition, because a strong dual condition is not satisfied, the algorithm may not be converged to a local optimal solution of the original problem, and may even diverge. On the other hand, the influence and effect caused by the access of a large number of distributed power supplies are not considered.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a distributed power distribution network congestion scheduling method considering a distributed power supply. The method of the invention considers the influence and the effect brought by the distributed power supply, gives full play to the flexible advantage of the distributed power supply, reduces the congestion degree of the network, and adopts the distributed method to solve and calculate the model, thereby being capable of quickly reducing the local congestion degree.
The invention provides a distributed power distribution network congestion scheduling method considering distributed power sources, which is characterized by comprising the following steps of:
1) Establishing a distributed distribution network congestion scheduling optimization model considering a distributed power supply, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
1-1) determining an objective function of the model, the expression is as follows:
Wherein Q isCiIs the total injected reactive power, P, of the reactive power compensation equipment of node iGi,QGiActive and reactive powers, C, respectively, of the distributed power supply at node iGiadjusting cost coefficient (related to actual needs and generally valued in positive real number domain) of active power output of distributed power supply at node i, delta PGithe active power output adjustment quantity of the distributed power supply at the node i is as follows:
Wherein the content of the first and second substances,The active output initial value of the distributed power supply at the node i is obtained;
1-2) determining constraint conditions of the model; the method comprises the following specific steps:
1-2-1) power flow constraint;
Establishing a power flow equation constraint of a power distribution network containing a distributed power supply, wherein the expression is as follows:
Wherein z isij＝rij+ixijRepresenting the impedance, r, of branch ijijresistance, x, representing branch ijijrepresents the reactance of branch ij; sij＝Pij+iQijRepresenting the complex power, P, of the sending end of branch ijijrepresenting the active power, Q, of the sending end of branch ijijrepresenting the reactive power of the sending end of the branch ij; sj＝Pj+iQjRepresenting the net injected complex power, P, of node jjnet injected active power, Q, representing node jjrepresents the net injected reactive power at node j; pDj,QDjRespectively representing the active load and the reactive load of the node j; vjRepresenting the magnitude of the voltage at node j.
1-2-2) system security constraints:
Wherein, | Iij|、respectively representing the current amplitude and the upper limit of the branch ij;P ij、 Q ij、Respectively representing an active lower limit, an active upper limit, a reactive lower limit and a reactive upper limit of a sending end of a branch ij; vi,Respectively representing the lower limit and the upper limit of the voltage amplitude of the node i;
V1＝Vref (6)
Wherein, V1Is the voltage amplitude, V, of node No. 1, the reference noderefis a set reference voltage amplitude;
1-2-3) distributed power supply operation constraints;
wherein the content of the first and second substances,Is the upper limit of the distributed power active output of node i,Q Gi,Respectively, a distributed power supply injection reactive lower limit and an upper limit of the node i,Q Ci,Respectively the lower limit and the upper limit of the reactive power equipment of the node i;
2) Relaxing the model established in the step 1), and equivalently obtaining the following model:
3) Dividing the whole power grid into R regions, and enabling Na,Ea,GaRespectively a node set, a branch set and a generator/reactive equipment set of the area a; note the bookfor global optimization variables belonging to node j, notean extension node set for the region a, the set consisting of NaAnd a node at the other end of the tie line; introducing variablesTo describe the local optimization variable of region a, which is the global variable sja local copy in region a; all inequality constraints in region a are in ga(x) To express, all equality constraints are in ha(x) Is expressed by f, the objective functiona(x) To represent; collectionIs a set of regions containing a node j in an extended node set of a region a, and the number of elements in the set, that is, the number of regions, is Mj:＝|MjL, |; and recording the node set at the junction of different areas as O: { j | Mj> 1 }; definition of x ═ (x)1,...,xR)TOptimizing variables for the whole situation; the model shown in equation (8) is converted into the following form:
Wherein x isaIs a state variable of the region a,are respectively asLower and upper limits of (d);
4) Performing distributed iterative solution on the model in the step 3) to obtain a distributed power distribution network congestion scheduling scheme considering the distributed power supply; the method comprises the following specific steps
4-1) making the initial iteration step number t equal to 1;
4-2) during the iteration of the t step, all nodes calculate local auxiliary variables:
wherein the content of the first and second substances,For the penalty factor at node a at the t-th iteration,the lagrangian multiplier corresponding to the constraint (11) during the iteration of the t step is defined;
4-3) all nodes calculate the local state variable of the iteration of the step t + 1:
4-4) all nodes calculate the local auxiliary variable of the iteration of the step t + 1:
4-5) calculating the original residual r of the t step iterationtAnd dual residual dt：
4-6) determining convergence using equation (16):
Wherein, deltatis the sum of the t-th iterationResidual errors, wherein epsilon is a preset convergence index and is more than 0;
If the formula (16) is satisfied, the model in the step 3) is converged, and Q corresponding to each node is obtained through solvingCiAnd QGiAnd obtaining a distributed distribution network congestion scheduling scheme considering the distributed power supply:
If the equation (16) does not hold, the model in the step 3) does not converge, all nodes calculate the penalty factor of the iteration of the t +1 step according to the equation (17), and then let t be t +1, and return to the step 4-2 again;
wherein μ ∈ (0,1), representing the dead zone size; τ > 0 is a constant representing the iteration step, tmaxis the maximum number of adjustments.
the invention has the advantages and beneficial effects that:
1. Compared with the traditional method, the distributed power distribution network congestion scheduling method considers the influence and the effect brought by the distributed power supply, gives full play to the flexible advantages of the distributed power supply, reduces the congestion degree of the network, and can quickly reduce the local congestion degree by adopting the distributed method to solve and calculate the model.
2. The distributed power distribution network congestion scheduling method uses a solving method of firstly carrying out convex treatment and then carrying out decomposition, so that the convergence is strictly ensured in theory, and the safe and stable operation of the system is ensured.
3. the method uses a penalty factor dynamic calculation method, has high convergence rate and high operation efficiency in practical application, and can meet the requirements of faster occasions.
4. The method and the system rely on the generators and the reactive compensation equipment in the network to carry out the power flow optimization, consider the active and reactive regulation capacity brought by the participation of the distributed power supply, are suitable for rapid power distribution network congestion scheduling, have low cost and are suitable for large-scale popularization.
Detailed Description
the invention provides a distributed power distribution network congestion scheduling method considering distributed power sources, which is further described in detail below by combining specific embodiments.
The invention provides a distributed power distribution network congestion scheduling method considering distributed power sources, which comprises the following steps:
1) establishing a distributed distribution network congestion scheduling optimization model considering a distributed power supply, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
1-1) determining an objective function of the model, the expression is as follows:
Wherein Q isCiis the total injected reactive power, P, of the reactive power compensation equipment of node iGi,QGiactive and reactive powers, C, respectively, of the distributed power supply at node iGiadjusting cost coefficient (related to actual needs and generally valued in positive real number domain) of active power output of distributed power supply at node i, delta PGithe active power output adjustment quantity of the distributed power supply at the node i is as follows:
Wherein the content of the first and second substances,Is the initial value of the active power output of the distributed power supply at the node i.
1-2) determining constraint conditions of the model; the method comprises the following specific steps:
1-2-1) power flow constraint;
establishing a power flow equation constraint of a power distribution network containing a distributed power supply, wherein the expression is as follows:
Wherein z isij＝rij+ixijRepresenting the impedance, r, of branch ijijResistance, x, representing branch ijijRepresenting the reactance of branch ij, where the symbol i follows the convention and is an imaginary symbol (subscript i represents the node); sij＝Pij+iQijRepresenting the complex power, P, of the sending end of branch ijijRepresenting the active power, Q, of the sending end of branch ijijRepresenting the reactive power of the sending end of the branch ij; sj＝Pj+iQjRepresenting the net injected complex power, P, of node jjnet injected active power, Q, representing node jjRepresents the net injected reactive power at node j; pDj,QDjRespectively representing the active load and the reactive load of the node j; vjrepresenting the magnitude of the voltage at node j.
1-2-2) system security constraints:
Wherein, | Iij|、Respectively representing the current amplitude and the upper limit of the branch ij;P ij、 Q ij、Respectively representing an active lower limit, an active upper limit, a reactive lower limit and a reactive upper limit of a sending end of a branch ij;V i,Respectively representing the lower and upper voltage amplitude limits of node i.
V1＝Vref (6)
wherein, V1is the voltage amplitude, V, of node No. 1, the reference noderefIs setthe reference voltage amplitude.
1-2-3) distributed power supply operation constraints;
the distributed power supply adopts a maximum power point tracking control mode, and in order to utilize the distributed power supply as far as possible and enable active power of all the distributed power supplies to be fully transmitted as far as possible, the voltage problem caused by the active power can be solved by adjusting reactive power of the distributed power supply and reactive power equipment.
Wherein the content of the first and second substances,is the upper limit of the distributed power active output of node i,Q Gi,respectively, a distributed power supply injection reactive lower limit and an upper limit of the node i,Q Ci,respectively the lower and upper reactive limits of the reactive equipment of node i.
2) relaxing the model established in the step 1), and equivalently obtaining the following model:
3) According to the connection relation between the electric power and the information of the actual power grid, the whole power grid can be divided into R areas, and N is ordereda,Ea,Garespectively, a node set, a branch set, and a generator/reactive equipment set for zone a. Note the bookIs a global optimization variable belonging to node j. Note the bookIs an extension of the area aSet of exhibition nodes, the set consisting of Naand a node at the other end of the connecting line. Introducing variablesto describe the local optimization variable of region a, which is the global variable sja local copy in area a. All inequality constraints in region a are in ga(x) To express, all equality constraints are in ha(x) Is expressed by f, the objective functiona(x) To indicate. CollectionIs a set of regions containing a node j in an extended node set of a region a, and the number of elements in the set, that is, the number of regions, is Mj:＝|MjL. And recording the node set at the junction of different areas as O: { j | Mj> 1 }. Definition of x ═ (x)1,...,xR)TVariables are optimized for the global situation. The model shown in equation (8) can be converted into the following form:
Wherein the content of the first and second substances,Is a state variable of the region a,are respectively aslower and upper limits of.
4) Performing distributed iterative solution on the model in the step 3) to obtain a distributed power distribution network congestion scheduling scheme considering the distributed power supply; the method comprises the following specific steps:
4-1) making the initial iteration step number t equal to 1;
4-2) during the iteration of the t step, all nodes calculate local auxiliary variables:
Wherein the content of the first and second substances,The value of the penalty factor at the node a in the iteration of the t step is generally in a range between (0,1),a Lagrange multiplier corresponding to the regional coupling constraint (11) in the t-th iteration;
4-3) all nodes calculate the local state variable of the iteration of the step t + 1:
4-4) all nodes calculate the local auxiliary variable of the iteration of the step t + 1:
4-5) calculating the original residual r of the t step iterationtand dual residual dt：
4-6) determining convergence using equation (16):
wherein, deltatthe total residual error of the t iteration is epsilon, which is a preset convergence index, and epsilon is more than 0;
if the formula (16) is established, the model in the step 3) is converged, and the distributed power distribution network congestion scheduling method considering the distributed power supply is finished; after the calculation is finished, Q corresponding to each node is obtainedCi(Total injected reactive of reactive compensation device of node i), QGiand (the reactive power output of the distributed power supply at the node i) is taken as a value, and each node reactive power compensation equipment or distributed power supply equipment only needs to output reactive power according to the corresponding value.
If the equation (16) does not hold, the model in the step 3) does not converge, all nodes calculate the penalty factor of the iteration of the t +1 step according to the equation (17), and then let t be t +1, and return to the step 4-2 again;
Wherein μ ∈ (0,1), representing the dead zone size; τ > 0 is a constant representing the iteration step, tmaxIs the maximum number of adjustments.
Claims (1)
1. A distributed power distribution network congestion scheduling method considering distributed power supplies is characterized by comprising the following steps:
1) Establishing a distributed distribution network congestion scheduling optimization model considering a distributed power supply, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
1-1) determining an objective function of the model, the expression is as follows:
Wherein Q isCiIs the total injected reactive power, P, of the reactive power compensation equipment of node iGi，QGiActive and reactive powers, C, respectively, of the distributed power supply at node iGiIs a section ofAdjusting cost coefficient, delta P, of distributed power supply active power output at point iGithe active power output adjustment quantity of the distributed power supply at the node i is as follows:
Wherein the content of the first and second substances,The active output initial value of the distributed power supply at the node i is obtained;
1-2) determining constraint conditions of the model; the method comprises the following specific steps:
1-2-1) power flow constraint;
Establishing a power flow equation constraint of a power distribution network containing a distributed power supply, wherein the expression is as follows:
wherein z isij＝rij+ixijRepresenting the impedance, r, of branch ijijresistance, x, representing branch ijijRepresents the reactance of branch ij; sij＝Pij+iQijRepresenting the complex power, P, of the sending end of branch ijijRepresenting the active power, Q, of the sending end of branch ijijrepresenting the reactive power of the sending end of the branch ij; sj＝Pj+iQjRepresenting the net injected complex power, P, of node jjnet injected active power, Q, representing node jjrepresents the net injected reactive power at node j; pDj，QDjRespectively representing the active load and the reactive load of the node j; vjRepresents the voltage amplitude of node j;
1-2-2) system security constraints:
wherein, | Iij|、respectively representing the current amplitude and the upper limit of the branch ij;P ij、 Q ij、respectively representing an active lower limit, an active upper limit, a reactive lower limit and a reactive upper limit of a sending end of a branch ij;V i，Respectively representing the lower limit and the upper limit of the voltage amplitude of the node i;
V1＝Vref (6)
wherein, V1is the voltage amplitude, V, of node No. 1, the reference noderefIs a set reference voltage amplitude;
1-2-3) distributed power supply operation constraints;
Wherein the content of the first and second substances,Is the upper limit of the distributed power active output of node i,Q Gi，respectively, a distributed power supply injection reactive lower limit and an upper limit of the node i,Q Ci，Respectively the lower limit and the upper limit of the reactive power equipment of the node i;
2) Relaxing the model established in the step 1), and equivalently obtaining the following model:
3) Dividing the whole power grid into R regions, and enabling Na，Ea，GaRespectively a node set, a branch set, a generator and a reactive equipment set of the area a; note the bookfor global optimization variables belonging to node j, notean extension node set for the region a, the set consisting of NaAnd a node at the other end of the tie line; introducing variablesto describe the local optimization variable of region a, which is the global variable sja local copy in region a; all inequality constraints in region a are in ga(x) To express, all equality constraints are in ha(x) Is expressed by f, the objective functiona(x) To represent; collectionis a set of regions containing a node j in an extended node set of a region a, and the number of elements in the set, that is, the number of regions, is Mj:＝|MjL, |; and recording the node set at the junction of different areas as O: { j | Mj> 1 }; definition of x ═ (x)1，...，xR)TOptimizing variables for the whole situation; the model shown in equation (8) is converted into the following form:
wherein x isaIs a state variable of the region a,x a，are respectively xalower and upper limits of (d);
4) performing distributed iterative solution on the model in the step 3) to obtain a distributed power distribution network congestion scheduling scheme considering the distributed power supply; the method comprises the following specific steps
4-1) making the initial iteration step number t equal to 1;
4-2) during the iteration of the t step, all nodes calculate local auxiliary variables:
Wherein the content of the first and second substances,For the penalty factor at region a at the t-th iteration,The lagrangian multiplier corresponding to the constraint (11) during the iteration of the t step is defined;
4-3) all nodes calculate the local state variable of the iteration of the step t + 1:
4-4) all nodes calculate the local auxiliary variable of the iteration of the step t + 1:
4-5) calculating the original residual r of the t step iterationtAnd dual residual dt：
4-6) determining convergence using equation (16):
Wherein, deltatThe total residual error of the t iteration is epsilon, which is a preset convergence index and is more than 0;
if the formula (17) is satisfied, the model in the step 3) is converged, and Q corresponding to each node is obtained through solvingCiAnd QGiAnd obtaining a distributed distribution network congestion scheduling scheme considering the distributed power supply:
If the equation (17) does not hold, the model in the step 3) does not converge, all nodes calculate the penalty factor of the iteration of the t +1 step according to the equation (18), and then let t be t +1, and return to the step 4-2 again;
Wherein μ ∈ (0,1), representing the dead zone size; τ > 0 is a constant representing the iteration step, tmaxIs the maximum number of adjustments.
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